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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "43f58c96",
+   "metadata": {},
+   "source": [
+    "# Uncertainty in seasonal forecasts for a single model system"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c7ce5c2e",
+   "metadata": {},
+   "source": [
+    "## Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48246700",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import calendar\n",
+    "\n",
+    "import numpy as np\n",
+    "import plotly.figure_factory as ff\n",
+    "import xarray as xr\n",
+    "from c3s_eqc_automatic_quality_control import diagnostics, download, utils"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5586e956",
+   "metadata": {},
+   "source": [
+    "## Define Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a3dfa6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Model\n",
+    "centre = \"ecmwf\"\n",
+    "system = \"51\"\n",
+    "\n",
+    "# Time\n",
+    "year_forecast = 2023\n",
+    "year_start_hindcast = 1993\n",
+    "year_stop_hindcast = 2016\n",
+    "month = 6\n",
+    "\n",
+    "# Region\n",
+    "region_name = \"Southern Norway\"\n",
+    "lat_slice = slice(64, 58)\n",
+    "lon_slice = slice(4, 14)\n",
+    "\n",
+    "# Download parameters\n",
+    "chunks = {\"year\": 1}\n",
+    "n_jobs = 1"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "62c65518",
+   "metadata": {},
+   "source": [
+    "## Define request"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ca06bda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "collection_id = \"seasonal-monthly-single-levels\"\n",
+    "\n",
+    "request = {\n",
+    "    \"format\": \"grib\",\n",
+    "    \"originating_centre\": centre,\n",
+    "    \"system\": system,\n",
+    "    \"variable\": \"2m_temperature\",\n",
+    "    \"product_type\": \"monthly_mean\",\n",
+    "    \"leadtime_month\": list(map(str, range(1, 7))),\n",
+    "    \"area\": [89.5, -179.5, -89.5, 179.5],\n",
+    "    \"grid\": \"1/1\",\n",
+    "    \"month\": f\"{month:02d}\",\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "51a4fdbf",
+   "metadata": {},
+   "source": [
+    "## Functions to cache"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b859c808-ea82-4f63-a6eb-bf58229c2e9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def regionalised_mean(ds, lon_slice, lat_slice, weights):\n",
+    "    ds = utils.regionalise(ds, lon_slice=lon_slice, lat_slice=lat_slice)\n",
+    "    ds = diagnostics.spatial_weighted_mean(ds, weights=weights)\n",
+    "    with xr.set_options(keep_attrs=True):\n",
+    "        ds[\"t2m\"] -= 273.15\n",
+    "    ds[\"t2m\"].attrs[\"units\"] = \"°C\"\n",
+    "    return ds"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "90606022",
+   "metadata": {},
+   "source": [
+    "## Download and transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a939f1e-7e91-4501-9ffc-1eca70253a36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datasets = {}\n",
+    "for model, years in {\n",
+    "    \"hindcast\": range(year_start_hindcast, year_stop_hindcast + 1),\n",
+    "    \"forecast\": [year_forecast],\n",
+    "}.items():\n",
+    "    ds = download.download_and_transform(\n",
+    "        collection_id,\n",
+    "        request | {\"year\": list(map(str, years))},\n",
+    "        chunks=chunks,\n",
+    "        n_jobs=n_jobs,\n",
+    "        transform_func=regionalised_mean,\n",
+    "        transform_func_kwargs={\n",
+    "            \"lon_slice\": lon_slice,\n",
+    "            \"lat_slice\": lat_slice,\n",
+    "            \"weights\": False,\n",
+    "        },\n",
+    "        backend_kwargs={\n",
+    "            \"time_dims\": (\n",
+    "                \"forecastMonth\",\n",
+    "                \"indexing_time\" if centre in [\"ukmo\", \"jma\", \"ncep\"] else \"time\",\n",
+    "            )\n",
+    "        },\n",
+    "        cached_open_mfdataset_kwargs={\n",
+    "            \"combine\": \"nested\",\n",
+    "            \"concat_dim\": \"forecast_reference_time\",\n",
+    "        },\n",
+    "    )\n",
+    "    datasets[model] = ds"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5ecce3e6",
+   "metadata": {},
+   "source": [
+    "## Density plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b2d38a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_limits(data, xfactor, yfactor):\n",
+    "    ylim = [0, max([max(d.y) for d in data]) * yfactor]\n",
+    "    xlim = [func([func(d.x) for d in data]) for func in (min, max)]\n",
+    "    xshift = abs(xlim[1] - xlim[0]) * xfactor\n",
+    "    xlim = [x + xshift * sign for x, sign in zip(xlim, (-1, 1))]\n",
+    "    return xlim, ylim\n",
+    "\n",
+    "\n",
+    "# Density plot for each lead time\n",
+    "for leadtime_month, ds_forecast in datasets[\"forecast\"].groupby(\"leadtime_month\"):\n",
+    "    ds_hindcast = datasets[\"hindcast\"].sel(leadtime_month=leadtime_month)\n",
+    "\n",
+    "    colors = [(26, 150, 65), (100, 50, 150)]\n",
+    "    values = [\n",
+    "        ds_hindcast[\"t2m\"].values.flatten(),\n",
+    "        ds_forecast[\"t2m\"].values.flatten(),\n",
+    "    ]\n",
+    "    labels = [centre + \" climatology\", centre + \" forecast\"]\n",
+    "    fig = ff.create_distplot(\n",
+    "        values,\n",
+    "        labels,\n",
+    "        show_hist=False,\n",
+    "        show_rug=True,\n",
+    "        colors=[f\"rgb{color}\" for color in colors],\n",
+    "        curve_type=\"kde\",\n",
+    "    )\n",
+    "    for color, data in zip(colors, fig.data):\n",
+    "        # Fill area under distline\n",
+    "        fig.add_scatter(\n",
+    "            x=data.x,\n",
+    "            y=data.y,\n",
+    "            fill=\"tozeroy\",\n",
+    "            mode=\"none\",\n",
+    "            fillcolor=f\"rgba{color + (.4,)}\",\n",
+    "            showlegend=False,\n",
+    "        )\n",
+    "\n",
+    "    xlim, ylim = get_limits(fig.data[:2], xfactor=0.03, yfactor=1.4)\n",
+    "    quantiles = np.quantile(values[0], [1 / 3, 2 / 3]).tolist()\n",
+    "    scatter_dicts = {\n",
+    "        \"lower tercile\": {\n",
+    "            \"color\": (0, 180, 250, 0.1),\n",
+    "            \"text\": \"COLD\",\n",
+    "            \"mask\": values[1] <= quantiles[0],\n",
+    "            \"xlim\": [xlim[0], quantiles[0]],\n",
+    "        },\n",
+    "        \"middle tercile\": {\n",
+    "            \"color\": (230, 230, 0, 0.1),\n",
+    "            \"text\": \"NEAR AVERAGE\",\n",
+    "            \"mask\": (values[1] > quantiles[0]) & (values[1] <= quantiles[1]),\n",
+    "            \"xlim\": quantiles,\n",
+    "        },\n",
+    "        \"upper tercile\": {\n",
+    "            \"color\": (250, 50, 0, 0.1),\n",
+    "            \"text\": \"WARM\",\n",
+    "            \"mask\": values[1] > quantiles[1],\n",
+    "            \"xlim\": [quantiles[1], xlim[1]],\n",
+    "        },\n",
+    "    }\n",
+    "    for i, (name, scatter_dict) in enumerate(scatter_dicts.items()):\n",
+    "        # Add background color and text\n",
+    "        fig.add_scatter(\n",
+    "            x=scatter_dict[\"xlim\"],\n",
+    "            y=[ylim[1]] * 2,\n",
+    "            fill=\"tozeroy\",\n",
+    "            mode=\"none\",\n",
+    "            fillcolor=f\"rgba{scatter_dict['color']}\",\n",
+    "            name=name,\n",
+    "        )\n",
+    "        percentage = 100 * scatter_dict[\"mask\"].sum() / values[1].size\n",
+    "        text_color = tuple(c // 2 for c in scatter_dict[\"color\"][:-1]) + (0.4,)\n",
+    "        fig.add_scatter(\n",
+    "            x=[sum(scatter_dict[\"xlim\"]) / 2],\n",
+    "            y=[ylim[1] * 0.98],\n",
+    "            mode=\"text\",\n",
+    "            name=\"\",\n",
+    "            text=f\"{scatter_dict['text']}<br>{round(percentage)}%\",\n",
+    "            textfont=dict(size=18, color=f\"rgba{text_color}\"),\n",
+    "            textposition=\"bottom center\",\n",
+    "            showlegend=False,\n",
+    "        )\n",
+    "\n",
+    "    # Title and labels\n",
+    "    forecast_reference_time = ds_forecast[\"forecast_reference_time\"].dt.date.values\n",
+    "    title = (\n",
+    "        f\"Density plot of {ds_forecast['t2m'].attrs['long_name']}\"\n",
+    "        f\" over {region_name} for {calendar.month_abbr[leadtime_month]} {year_forecast},\"\n",
+    "        f\"<br>from {centre} with start time {forecast_reference_time}.\"\n",
+    "        f\" Hindcast period from {year_start_hindcast} to {year_stop_hindcast}.\"\n",
+    "    )\n",
+    "    fig.update_layout(\n",
+    "        title=dict(text=title, font={\"size\": 22}),\n",
+    "        yaxis_range=ylim,\n",
+    "        xaxis_range=xlim,\n",
+    "        font_size=17,\n",
+    "        autosize=False,\n",
+    "        width=900,\n",
+    "        height=500,\n",
+    "    )\n",
+    "    fig.update_xaxes(\n",
+    "        title_text=(\n",
+    "            f\"Mean {ds_forecast['t2m'].attrs['long_name']}\"\n",
+    "            f\" ({ds_forecast['t2m'].attrs['units']})\"\n",
+    "        )\n",
+    "    )\n",
+    "    fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}